A cooperative control problem where N Unmanned Aerial Vehicles (UAVs) are assigned to eliminate M main targets is studied in this paper. The environment where these assignments are performed also includes T >> M threats that pose a risk to the group of UAVs. A decentralized optimization problem is presented where agents try to minimize a total cost that combines distance to reach main targets and exposure to threats. The main novelty here is the use of limited resources by the UAVs to eliminate threats while on their way to reach main targets. This option improves the trajectories of vehicles by further reducing total cost but it adds complexity to the optimization problem. The solution to this extended problem provides optimal decisions regarding the selection of threats to eliminate, and the corresponding trajectories to follow, in order to minimize total cost. Due to constraints imposed by communication topologies, agents implement a decentralized auction scheme in order to assign UAVs to threats while avoiding conflicts on those assignments. This paper describes the specific constraints concerning the decentralized decision process and its effects in recomputing new costs. The paper also offers preliminary results that provide coordinated selection of UAV paths and choice of threats to attack.
- Dynamic Systems and Control Division
UAV Coordinated Decision Making and Mission Management
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Garcia, E, & Casbeer, DW. "UAV Coordinated Decision Making and Mission Management." Proceedings of the ASME 2014 Dynamic Systems and Control Conference. Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems. San Antonio, Texas, USA. October 22–24, 2014. V001T14A001. ASME. https://doi.org/10.1115/DSCC2014-5878
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